Scientists may soon stop hunting for new materials — and start designing them to order.
For the first time, Northwestern University scientists have demonstrated that megalibraries — that dramatically accelerate materials discovery — can do more than uncover promising new materials . It can also help scientists intentionally engineer those new materials with specific properties.
In a new study, the team challenged the megalibrary platform to search through thousands of chemical combinations to pinpoint a promising piezoelectric candidate, a material that generates electricity when pressed, bent or squeezed. Then, the researchers used the platform to deliberately design a piezoelectric material that operated at a specific temperature. The platform was not only successful but also incredibly fast, enabling the design of a promising candidate material within hours.
This advance points toward a future where scientists can move beyond the traditionally slow trial-and-error approach to rapidly designing, synthesizing and testing materials with tailored properties. Just as importantly, the platform can generate the vast, high-quality datasets needed to train artificial intelligence (AI) systems to help discover the next generation of materials.
The study will be published today (May 22) in the journal Science Advances.
“With the megalibrary format, we can synthesize materials faster than has ever been contemplated before,” said Northwestern’s Chad A. Mirkin , who invented and developed the platform with colleagues at Mattiq , a materials-discovery startup that uses megalibraries for AI. “We have developed a screening capability based on a technique called second harmonic generation (SHG) microscopy that allows researchers to review more than a million different material samples in less than 30 minutes. In this study, we show we can not only build a library of a million different materials, but we also can interrogate them at the individual particle level. We’re about to witness the meteoric rise of materials discovery, and this is just the start.”
A nanotechnology pioneer, Mirkin is the George B. Rathmann Professor of Chemistry, a professor of medicine (hematology and oncology) and a professor of chemical and biological engineering, biomedical engineering and materials science and engineering at Northwestern, where he has appointments at the Weinberg College of Arts and Sciences , Northwestern University Feinberg School of Medicine and McCormick School of Engineering . He is also the founding executive director of the International Institute for Nanotechnology .
A faster alternative to autonomous labs
First introduced by Mirkin’s team in 2016, the megalibrary platform can condense the years-long search for new materials into a single day. By simultaneously synthesizing millions of tiny material candidates on a single chip, the platform allows scientists to explore chemical possibilities at a scale impractical with conventional trial-and-error methods.
Mirkin contrasted this approach with emerging “self-driving labs,” automated systems that use robotics and AI to propose, develop and test new materials iteratively. Those platforms typically work in a step-by-step manner, refining one experiment after another. But the megalibrary takes a massively parallel approach, generating and evaluating enormous numbers of candidates simultaneously.
“Compared to the megalibrary, which moves at a sprint, self-driving labs are basically crawling,” said Jarod Beights, a graduate student in the Mirkin Research Group and the study’s co-first author. “Those labs cannot compete with our speeds and cannot compete with the generation of data, which is absolutely essential for training AI algorithms.”
Designing materials with purpose
After demonstrating the platform’s ability to discover new materials, Mirkin wanted to use the megalibrary to design a material with a specific behavior. To meet this goal, Mirkin’s team focused on piezoelectric materials, which are used in a range of technologies, from ultrasound imaging and sensors to motion detectors and energy-harvesting devices. Using the platform, the researchers identified a previously unknown, chemically complex material that would have been extraordinarily difficult to find through conventional experimentation or the more iterative discovery approaches used by emerging self-driving labs.
But the bigger advance came next.
By analyzing how subtle changes in chemical composition affected performance, Mirkin’s team uncovered a useful relationship between material composition and operating temperature. Using that insight, the researchers engineered a piezoelectric material designed to maintain its function up to 80 degrees Celsius (176 degrees Fahrenheit). The ability to tune a material’s performance means scientists can begin tailoring materials for specific technologies and operating conditions, including temperature sensitive devices.
Fueling AI-driven discovery
Beyond materials discovery, the platform helps address a growing challenge in AI-driven science: the need for large high-quality datasets built from real-world experiments. AI systems are only as powerful as the datasets used to train them. While scientists increasingly can automate materials synthesis, rapidly collecting meaningful information about how those materials behave has remained a major bottleneck. The megalibrary could help overcome that challenge.
By rapidly generating and screening vast numbers of materials, the platform can produce massive datasets linking chemistry to performance. Machine-learning algorithms need this type of structured information to identify hidden patterns, predict promising candidates and accelerate the future of discovery.
“We’ve developed a screening capability that allows researchers to look at literally a million different materials, generating a million data points,” said the study’s co-first author Jun Li, a former Northwestern postdoctoral fellow who is now an assistant professor of mechanical engineering at the University of Colorado Boulder. “We can use that data to train algorithms.”
Mirkin envisions extending the megalibrary approach across many types of materials and properties, helping build the data infrastructure for the next era of AI-assisted materials design.
“We’ve found materials for piezoelectrics, catalysis and photocatalysis, and we’re going to continue discovering materials across the board,” Mirkin said. “We’re going to repeat this process to find materials for batteries, for fusion, for optics. Our world depends on new materials, and we’ve only explored a tiny fraction of materials possibilities so far.”
Science Advances
High entropy 1D halide perovskite piezoelectrics discovered by megalibrary synthesis and rapid nonlinear optical screening
22-May-2026
Mirkin has financial interests in and affiliations with Mattiq, Inc. Northwestern University has financial interests (e.g., royalties) in Mattiq, Inc.